Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.

中文翻译：

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一种改进的自适应并行遗传算法求解机场大门分配问题

登机口分配问题（GAP）是机场运营管理的核心问题。但是，由于机场登机口资源有限和飞行规模的增加，导致登机口分配存在严重问题。在本文中，为了为大型GAP提供决策支持，建立了基于登机口分配规则（例如，航班类型约束，安全时间间隔约束和邻接冲突约束）的模型来表达此问题。然后设计一种改进的自适应并行遗传算法（APGA）来求解模型。该算法之所以有效，是因为它引入了精英策略和并行设计的思想，并且可以自适应地调整交叉概率。此外，提出了不同的实例来演示所提出的算法。将该算法与标准遗传算法和CPLEX算法的计算结果进行了比较，表明该算法性能较好，计算时间较短。另外，通过对大规模飞行数据的重复实验，验证了算法的稳定性和实用性。